Conclusion

7

Summary

During the first half of my PhD work I have recorded audio and video data of wildlife geese during landing, foraging and flushing behaviour. The system and results of this has been presented in the conference paperA Multimedia Capture System for Wildlife Studies[45]. Based on the data, I have developed a pattern recognition algorithm for automatic detection of goose behaviour. This work is presented in the paperA Vocal based Analytical Method for Goose Behaviour Recognition[50].

The algorithm is however based on limited data, as it was not possible to record many occurences of wildlife geese in the short timespan they where active¹. More data is therefore being recorded this spring (2012) to verify or improve the existing model.

Furhtermore a vision based approach has been developed in fall, 2011. This work is currently being drafted, and will be submitted to a journal in the near future. Based on these algorithms current research is focused on Audio-Visual recognition, which combines the two information streams. This is inspired by the work within Audio-Visual speech recognition, where a combina-tion of the two gives high classificacombina-tion results.

One way of combining the two is through classifier fusion, which has been implemented in a system, which is currently being tested in a real life scenario. This test includes the audio and video based models, which has been developed during the first half of my PhD, and an actuator (speakers) for communicating with the geese. The expected result of this test is to verify the models in a real life scenario, and to investigate the effect of wildlife communication, with respect to wildlife management.

1The recordings took place in spring 2011, where the weather suddenly got very warm and the geese flew towards Norway

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